University of PennsylvaniaAnna Leshinskaya & Sharon L. Thompson-Schill

Gopnik, A., & Sobel, D. M. (2000). Detecting blickets: how young children use information about novel causal powers in categorization and induction. Child Development, 71(5), 1205–1222. Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114(2), 165–96.

Structured knowledge and novel object kinds can be inferred from visual event streams

One way to achieve abstraction from sensory experiences is to encode relations among sensory events; a basic one being the direction of prediction ( ‘before’ vs ‘after’).

To what extent do adult learners do this spontaneously in naturalistic and bottom-up fashion?

Statistical information should spontaneously inform con-ceptual judgment and category formation without a top-down instructional context.

Relational schemas should operate fairly automatically during event processing.

e�ect

cause

frequent

rare

static

.02

.43

.53.02

.36

.35

.25

.04.70.01

.10

.04

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.92

.05.02

.02

.49

.01

.25

.25

.15

e�ect

cause

rare

static

.02

.43

.53

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.04.70.01

.10

.04

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.93

.05.02

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thale sibbie

causality ratings

Task: pay close attention and try to predict what will happen next (press a key when something unexpected occurs).

2.4 s 1.2 s

Stimuli: sequence of 250 animated visual events for 2 objects, order governed by markov chain.

Experiment 1: How objects attain causal properties from naturalistic event streams

Experiments 2 & 3: Relational schemas are deployed automatically

Experiment 4: Unsupervised induction of causal kinds, generalizing across manner of

causation

cause

effect

light-causers light-reactors

cause

effect

cause

effect

cause

effect

shape to condition assignments and effect events counterbalanced

training objects

1 2 1 2

task same as Experiment 1 with one interim non-specific test

similarity judgmentsfollowing training using a spatial sorting task

Linear regression with motion model : c cells = 0 , others = ; causal model : m cells = 0 , others = 1, fit on individual data, betas subjected to t-test.

Linear regression with a single factor:mixture model: c cells = .5 and m cells = .5, others = 1

more sim

ilarm

ore di�erent

light causer 1

light causer 2

light reactor 1

light reactor 2

light causer 1light causer 2light reactor 1light reactor 2

C

C

m

m

generalization testwhich objects is this most similar to?

same motion & same causality

different motion & same causality

same motion & different causality

different motion & different causality

cause+ motion+

cause+ motion-

cause- motion+

cause- motion-

0 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Percent of Subjects Choosing

n=18

Motion match F(1,17) = 15.36, p < .001Causal match F(1,17) = 5.52, p < .05

Casual modelt(17) = 2.42, p < .05

Motion model t(17) = 2.39, p <.05

Mixture model t(17) = 3.53, p < .01

rare

common

common

common

rare

response

1.2s

oddball decision task

obj

ect 1

obj

ect 2

different-relation condition

same-relation condition

shape to condition assignments randomizedeffect event (light, bubbles, confetti) counterbalanced

order of training objects randomized

familiarity forced choice teststrong transitions compared to weaker transitions

administered following all learning exposure

which of the two videos is more typical/familiar?

oddball events on 10% of trials

obj

ect 1

obj

ect 2

different-relation condition

same-relation condition

obj1 obj2 obj1 obj20

0.10.20.30.40.50.60.70.8 ** **

*

different-relation same-relation

cause to e�ectThales tilting causes the light to flash.

e�ect to causeThe light flashing causes thales to tilt.

object to e�ectThales cause the light to flash.

object to frequentThales cause the stars to appear.

1 2 3 4 5definitely false definitely trueunsure

1

1.5

2

2.5

3

3.5

4

4.5

5

caus

e-e�

ect

e�ec

t-cau

se

obje

ct-e

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***

** *****

**

**

n = 57n = 24

n = 14

** p <.01 *** p< .001

Main effect of condition F(1,70) = 5.14, p < .05

- Higher (40%) noticing rate due to slight changes in the task. Thus, par-ticipants split into noticers and non-noticers using freeform responses post-task.

-Training object 2 was always tested first.

-Same-relation condition: ‘cause’ event was ambient for both videos, or object-based for both videos.

Subjects did not have explicit access to this knowledge.

object 1 object 2 object 1 object 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

same-relation different-relation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

object 1 object 2 object 1 object 2

same-relation different-relation

noticers non-noticers

Main effect of condition F(1,47) = 12.17, p = .001

Main effect of condition F(1,82) = 8.00 p = .006

Noticing by Condition interaction

F(1,129) = 17.80, p < .0001

It should be noted that we did not see this effect in a previ-ous version of this

task (with some dif-ferences) and thus aim to replicate it.

**

**

familiarity forced choice test

(by training order)

oddball decision task

Why is this important?

Predictive structure is a pervasive part of experience that can be extracted using straightforward learning mechanisms. But it can also be leveraged to gain abstraction, as predictive relations can be generalized across participating events and sensory features. Together, this could account for bottom-up abstraction of sen-sory experience and the formation of novel kinds generalizing across sensory features. do

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normalized screen distances

compared only in participants accurate on non-causal judgments of the relevant statistic